DISCRETE AND CONTINUOUS doi:10.3934/dcdsb.2020191 DYNAMICAL SYSTEMS SERIES B Volume 26, Number 5, May 2021 pp. 2479{2497 ABSTRACT SIMILARITY, FRACTALS AND CHAOS The art of doing mathematics consists in finding that special case which contains all the germs of generality. David Hilbert Marat Akhmet∗ Department of Mathematics, Middle East Technical University, Ankara, Turkey Ejaily Milad Alejaily Department of Fundamental Sciences, College of Engineering Technology, Houn, Lybya (Communicated by Miguel Sanjuan) Abstract. A new mathematical concept of abstract similarity is introduced and is illustrated in the space of infinite strings on a finite number of symbols. The problem of chaos presence for the Sierpinski fractals, Koch curve, as well as Cantor set is solved by considering a natural similarity map. This is accom- plished for Poincar´e,Li-Yorke and Devaney chaos, including multi-dimensional cases. Original numerical simulations illustrating the results are presented. 1. Introduction. Self-similarity is one of the most important concepts in modern science. From the geometrical point of view, it is defined as the property of objects whose parts, at all scales, are similar to the whole. Dealing with self-similarity goes back to the 17th Century when Gottfried Leibniz introduced the notions of recursive self-similarity [47]. Since then, history has not recorded any thing about self-similarity until the late 19th century when Karl Weierstrass introduced in 1872 a function that being everywhere continuous is nowhere differentiable. The graph of the Weierstrass function became an example of a self-similar curve. The set constructed by Georg Cantor in 1883 is considered as the most popular self similar set since it is a simple and perfect example for theory and applications of this field. Space-filling curves are substantial epitomai of continuous self-similar curves which were described by Giuseppe Peano and David Hilbert in 1890-91. Other examples of self-similar sets are Koch curve discovered by Helge von Koch in 1904 and Sierpinski gasket and carpet which are introduced by Waclaw Sierpinski in 1916. Julia sets gained significance in being generated using the dynamics of iterative function. They are discovered by Gaston Julia and Pierre Fatou in 1917-19, when 2020 Mathematics Subject Classification. Primary: 28A80, 65P20; Secondary: 37B10. Key words and phrases. Abstract self-similarity, self-similar space, similarity map, fractals, chaos, multi-dimensional chaotic maps. The first author has been supported by a grant (118F161) from TUB¨ ITAK,_ the Scientific and Technological Research Council of Turkey. ∗ Corresponding author: Tel.: +90 312 210 5355, Fax: +90 312 210 2972. 2479 2480 MARAT AKHMET AND EJAILY MILAD ALEJAILY they studied independently the iteration of rational functions in the complex plane. The term \fractal" was coined by Benoit Mandelbrot in 1975 to describe certain geometrical structures that exhibit self-similarity. Since then, this word has been employed to denote all the above mentioned sets, and the field became known as fractal geometry. Consequently, the fractal concept is axiomatically linked with the notion of self-similarity which is considered to be one of the acceptable definitions of fractals. That is, a fractal can be defined as a set that displays self-similarity at all scales. However, Mandelbrot defined a fractal as a set whose Hausdorff dimension is strictly larger than its topological dimension [30]. To sum up, self-similarity and fractional dimension are the two most important features of fractals. The connection between them is that self-similarity is the easiest way to construct a set that has fractional dimension [12]. Chaos, in general, can be defined as aperiodic long-term behavior in a determin- istic system that exhibits sensitive dependence on initial conditions [25]. The first recognition of chaos phenomenon was indicated in the work of Henri Poincar`ein 1890 when he studied the problem of the stability of the solar system. In the 1950s, Edward Lorenz discovered sensitivity to initial conditions in a weather forecasting model. This property is considered as a main ingredient of chaos. There are different types and definitions of chaos. Devaney [13] and Li-Yorke [28] chaos are the most frequently used types, which are characterized by transitivity, sensitivity, frequent separation and proximality. Another common type occurs through period-doubling cascade which is a sort of route to chaos through local bifurcations [19, 39, 41]. In the papers [2,3], Poincar`echaos was introduced through the unpredictable point concepts. Further, it was developed to unpredictable functions and sequences. Several researches pointed out that a close relationship between chaos and fractal geometry can be observed. It can be seen, for instant, in the dynamics of Fatou- Julia iteration used to construct Julia and Mandelbrot sets where two neighbor points in the domain which are close to the boundary may have completely different behavior. That is, we can say about sensitivity in fractal structures. Chaos tells us about the state of irregularity and divergence of trajectories which depend in the nature of the dynamics, whereas the fractal concept can be used to study complex geometric structures. Therefore, the interlink between chaos and fractals is more clear when fractal dimension is used to measure the extent to which a trajectory fills its phase space. In other words, fractal dimension of the orbit in phase space implies the existence of a strange attractor [33]. The fundamental work on the chaotic nature of fractals has been done only for specific types categorized under the totally disconnected fractals [8, 11, 13]. In that work, the topological conjugacy concept was utilized to prove that these fractal sets are invariant for certain chaotic maps. Except for that, relatively few studies have been carried out on chaotic dynamical systems for fractals, and perhaps the most relevant one is what have been done on the Sierpinski carpet in [10]. In that research, the author shows that the dynamical system associated with a shift transformation defined on the Sierpinski carpet set is chaotic in the sense of topological mixing. Our results have a special importance that we do not utilize topological conjugacy to prove the presence of chaos in fractal sets. Moreover, we consider different types of chaos, namely, Devaney, Li-Yorke and Poincar`e,and the results cover several kinds of self- similar fractals such as Cantor sets, Sierpinski fractals and Koch curve. Chaos in fractals, particularly Sierpinski carpet and Koch curve, has not been considered in ABSTRACT SIMILARITY, FRACTALS AND CHAOS 2481 the literature before. Furthermore, our approach is applicable for sets in multi- dimensional spaces. A good example is the logistic map. It was shown that a chaos equivalent to Li-Yorke type can be extended to higher-dimensional discrete systems [14, 15, 31]. This requires employing special theorems like Marotto Theorem [31]. Applying chaotic abstract similarity developed in our paper, we have shown that Devaney, Li-Yorke and Poincar`echaos can take place in the dynamics of finite set of connected and perturbed logistic maps. Examples with numerical simulations are provided in Section5. In this paper we concern with self-similarity. This concept is reflected in many problems that arise in various fields such as wavelets, fractals, and graph systems [24]. The idea of a self-similar set was first considered by Moran in 1946 [34] who gave a mathematical definition of a geometric construction as a collection of sets sat- isfying specific conditions. The Moran's construction has been studied extensively and developed by many researcher proposing various methods. In [37, 38, 43], the approach was generalized to build different classes of Moran-like geometric con- struction with the help of a symbolic space. The topological conjugacy to symbolic dynamical system was employed to describe the symbolic representation of the con- structions. This type of self-similar sets is referred to as Cantor-like set. Interesting definitions of self-similarity and related problems of dimension and measure are dis- cussed in papers [7, 16, 17, 18, 20, 23, 26, 35, 42]. The manuscripts consider sets in Euclidean space Rn and define self-similar set as a union of its images under similarity transfunctions [18]. Our research develops self-similarity based on point- set structure in metric spaces and this why we call it abstract self-similarity. The development does not rely on any special functions, and the similarity map used in our paper is applied for chaos formation. The map is a natural consequence of the structure of the domain and it can be considered as a generalization of the Bernoulli shift on symbolic spaces. Our notion of similarity map, therefore, is different from the classical ones essentially considered in literature. In the present paper, we are not concerned in analysis of dimension and measure but mostly rely on the dis- tance, since our main goal is to discuss chaos problem for fractals. Nevertheless, we suppose that our suggestions may be useful for the next extension of the results ob- tained in [7, 16, 17, 18, 20, 23, 26, 35, 42] for the abstract self-similarity case. First of all those which consider dimension and measure and corresponding problems of fractals. We define a self-similar set as a collection of points in a metric space, where it can be considered as a union of infinite shrinking sets with notation that allows to introduce dynamics in the set and then prove chaos which is the most important result of the present research. For the purpose, a specific map over the invariant self-similar set is defined. The map acting as the identity, if the whole set taken as an argument for the map. This feature is equivalent to the property of self-similarity in the ordinary fractals, and this is the reason for calling this map the similarity map.